ConversationMoC: Encoding Conversational Dynamics using Multiplex Network for Identifying Moment of Changein Mood and Mental Health Classification

<p> Understanding mental health conversation dynamics is crucial, yet prior studies often overlooked the intricate interplay of social interactions. This paper introduces a unique conversation-level dataset and investigates the impact of conversational context in detecting Moments of Change (MoC) in individual emotions and classifying Mental Health (MH) topics in discourse. In this study, we differentiate between analyzing individual posts and studying entire conversations, using sequential and graph-based models to encode the complex conversation dynamics. Further, we incorporate emotion and sentiment dynamics with social interactions using a graph multiplex model driven by Graph Convolution Networks (GCN). Comparative evaluations consistently highlight the enhanced performance of the multiplex network, especially when combining reply, emotion, and sentiment network layers. This underscores the importance of understanding the intricate interplay between social interactions, emotional expressions, and sentiment patterns in conversations, especially within online mental health discussions. We are sharing our new dataset (ConversationMoC) and models with the broader research community to facilitate further research. </p>